# Required Packages
import pandas as pd
import numpy as np

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## missingno
import missingno as msno

## seaborn
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (17, 6)
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 12
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['text.color'] = 'k'
%matplotlib inline

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
%config InlineBackend.figure_format = 'retina' 

import warnings
warnings.filterwarnings("ignore")

fig, ax = plt.subplots(1, 1, figsize= (16,2))
c1 = 'Maroon'
c2 = 'LightCoral'
edc = 'DarkRed'
_ = ax.text(0.4, .6, "Lending Club", color = c1,
            fontdict = {'family': 'fantasy','color':  'darkred','weight': 'normal', 'size': 50},
            rotation=0.,ha="center", va="center", bbox=dict(boxstyle="round", ec= edc, fc=c2))

_ = ax.text(.745, .25, "Dataset", color = c2,
            fontdict = {'family': 'fantasy','color':  'darkred','weight': 'normal', 'size': 50},
            rotation=0.,ha="center", va="center", bbox=dict(boxstyle="roundtooth", ec= edc, fc=c1))
_ = ax.axis('tight')
_ = ax.axis('off')

In this article, we analyze and create a predictive model for Lending Club Data which is available as a part of the creditmodel: Toolkit for Credit Modeling, Analysis and Visualization R Package. Similar datasets also can be accessed from the lendingclub website as well.

Description¶

This data contains complete loan data for all loans issued through the period stated, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information. The data containing loan data through the "present" contains complete loan data for all loans issued through the previous completed calendar quarter(period: 2018Q1:2018Q4).

data_dict = {'id': 'A unique LC assigned ID for the loan listing',
             'issue_d': 'The month which the loan was funded',
             'loan_status': 'Current status of the loan',
             'addr_state': 'The state provided by the borrower in the loan application',
             'acc_open_past_24mths': 'Number of trades opened in past 24 months',
             'all_util': 'Balance to credit limit on all trades',
             'annual_inc': 'The self:reported annual income provided by the borrower during registration',
             'avg_cur_bal': 'Average current balance of all accounts',
             'bc_open_to_buy': 'Total open to buy on revolving bankcards',
             'bc_util': 'Ratio of total current balance to high credit/credit limit for all bankcard accounts',
             'dti': """A ratio calculated using the borrower's total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower's self:reported monthly income""",
             'dti_joint': """A ratio calculated using the co:borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co:borrowers' combined self:reported monthly income""",
             'emp_length': """Employment length in years Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years""",
             'emp_title':'The job title supplied by the Borrower when applying for the loan',
             'funded_amnt_inv': 'The total amount committed by investors for that loan at that point in time',
             'grade': 'LC assigned loan grade',
             'inq_last_12m': 'Number of credit inquiries in past 12 months',
             'installment': 'The monthly payment owed by the borrower if the loan originates',
             'max_bal_bc': 'Maximum current balance owed on all revolving accounts',
             'mo_sin_old_il_acct': 'Months since oldest bank installment account opened',
             'mo_sin_old_rev_tl_op': 'Months since oldest revolving account opened',
             'mo_sin_rcnt_rev_tl_op': 'Months since most recent revolving account opened',
             'mo_sin_rcnt_tl': 'Months since most recent account opened',
             'mort_acc': 'Number of mortgage accounts',
             'pct_tl_nvr_dlq': 'Percent of trades never delinquent',
             'percent_bc_gt_75': 'Percentage of all bankcard accounts > 75',
             'purpose': 'A category provided by the borrower for the loan request',
             'sub_grade': 'LC assigned loan subgrade',
             'term': 'The number of payments on the loan Values are in months and can be either 36 or 60',
             'tot_cur_bal': 'Total current balance of all accounts',
             'tot_hi_cred_lim': 'Total high credit/credit limit',
             'total_acc': """The total number of credit lines currently in the borrower's credit file""",
             'total_bal_ex_mort': 'Total credit balance excluding mortgage',
             'total_bc_limit': 'Total bankcard high credit/credit limit',
             'total_cu_tl': """Number of finance trades""",
             'total_il_high_credit_limit': """Total installment high credit/credit limit""",
             'verification_status_joint': """Indicates if the co:borrowers' joint income was verified by LC, not verified, or if the income source was verified""",
             'zip_code': """The first 3 numbers of the zip code provided by the borrower in the loan application"""}

Data = pd.read_csv('LC_Data/lendingclub.csv', index_col = 0)
Data.head(10)

The dataset dictionary also can be found here.

Feature	Description
id	A unique LC assigned ID for the loan listing
issue_d	The month which the loan was funded
loan_status	Current status of the loan
addr_state	The state provided by the borrower in the loan application
acc_open_past_24mths	Number of trades opened in past 24 months
all_util	Balance to credit limit on all trades
annual_inc	The self:reported annual income provided by the borrower during registration
avg_cur_bal	Average current balance of all accounts
bc_open_to_buy	Total open to buy on revolving bankcards
bc_util	Ratio of total current balance to high credit/credit limit for all bankcard accounts
dti	A ratio calculated using the borrower's total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower's self:reported monthly income
dti_joint	A ratio calculated using the co:borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co:borrowers' combined self:reported monthly income
emp_length	Employment length in years Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years
emp_title	The job title supplied by the Borrower when applying for the loan
funded_amnt_inv	The total amount committed by investors for that loan at that point in time
grade	LC assigned loan grade
inq_last_12m	Number of credit inquiries in past 12 months
installment	The monthly payment owed by the borrower if the loan originates
max_bal_bc	Maximum current balance owed on all revolving accounts
mo_sin_old_il_acct	Months since oldest bank installment account opened
mo_sin_old_rev_tl_op	Months since oldest revolving account opened
mo_sin_rcnt_rev_tl_op	Months since most recent revolving account opened
mo_sin_rcnt_tl	Months since most recent account opened
mort_acc	Number of mortgage accounts
pct_tl_nvr_dlq	Percent of trades never delinquent
percent_bc_gt_75	Percentage of all bankcard accounts > 75
purpose	A category provided by the borrower for the loan request
sub_grade	LC assigned loan subgrade
term	The number of payments on the loan Values are in months and can be either 36 or 60
tot_cur_bal	Total current balance of all accounts
tot_hi_cred_lim	Total high credit/credit limit
total_acc	The total number of credit lines currently in the borrower's credit file
total_bal_ex_mort	Total credit balance excluding mortgage
total_bc_limit	Total bankcard high credit/credit limit
total_cu_tl	umber of finance trades
total_il_high_credit_limit	Total installment high credit/credit limit
verification_status_joint	Indicates if the co:borrowers' joint income was verified by LC, not verified, or if the income source was verified
zip_code	The first 3 numbers of the zip code provided by the borrower in the loan application

def Colors (x, N = 6):
    C = sns.color_palette("RdYlGn", N)
    r = np.linspace(0, 100, N)
    for i in range(N-1):
        if r[i] <=  x < r[i+1]:
            return C[i]
        else:
            return C[-1]

def Data_info(Inp, Plot = True, FS = (16,6)):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out ['Size'] = Inp.shape[0]
    Out['Percentage'] = 100 - np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    Out.index.name = 'Features'
    Clr = Out['Percentage'].map(lambda x: Colors(x)).tolist()
    fig, ax = plt.subplots(1, 1, figsize= FS)
    Temp = Out['Percentage'].to_frame()
    _ = Temp.plot(kind='bar', width = 1, color= [Clr], edgecolor = 'white', hatch = '///', legend=None, alpha=0.75, ax = ax)
    _ = Temp.plot(kind='bar', width = 1, color='None', edgecolor = 'Black', legend=None, alpha=1, lw=1., ax = ax)
    _ = ax.set_ylim(0, 101)
    _= plt. grid(b=False, which='both', axis='both')
    _= plt. grid(b=True, which='major', axis='y')
    return Out

def dtypes_group(Inp):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out.index.name = 'Data Type'
    Out = Out.reset_index(drop = False)
    Out['Data Type'] = Out['Data Type'].astype(str)
    return Out

display(dtypes_group(Data).style.hide_index())
_ = Data_info(Data)

Exploratory Data Analysis¶

Number of Loan Listing by the State Provided by the Borrower In the Loan Application¶

Feat= 'addr_state'
Group = Data[Feat].value_counts().to_frame('Count').sort_index().reset_index().rename(columns = {'index': Feat})

fig = go.Figure(data=go.Choropleth(locations= Group[Feat],
                                   z = Group['Count'].astype(float),
                                   locationmode = 'USA-states',
                                   colorscale = 'PuBu'))
fig.update_layout(title={'text': 'Number of Loan Listing by State', 'x':0.46, 'y':0.9, 'xanchor': 'center', 'yanchor': 'top'},
                  geo_scope='usa')
fig.show()

display(Group.sort_values(by=['Count'],
                          ascending=False).set_index(Feat).head(25).T.style.hide_index().background_gradient(cmap='Blues'))

Number of Applications from Each State and the Application A|pproval Status¶

Feat= 'addr_state'
Group = Data.groupby([Feat,'loan_status'])[Feat].agg('count').to_frame('Count').reset_index()

fig = go.Figure(data=go.Choropleth(locations= Group.loc[Group['loan_status'] == 1, Feat],
                                   z = Group.loc[Group['loan_status'] == 1, 'Count'].astype(float),
                                   locationmode = 'USA-states',
                                   colorscale = 'Greens'))
fig.update_layout(title={'text': 'Number of Approved Loans by State',
                         'x':0.46, 'y':0.9, 'xanchor': 'center', 'yanchor': 'top'},
                  geo_scope='usa')
fig.show()

fig = go.Figure(data=go.Choropleth(locations= Group.loc[Group['loan_status'] == 0, Feat],
                                   z = Group.loc[Group['loan_status'] == 0, 'Count'].astype(float),
                                   locationmode = 'USA-states',
                                   colorscale = 'Reds'))
fig.update_layout(title={'text': 'Number of Not Approved Loans by State',
                         'x':0.46, 'y':0.9, 'xanchor': 'center', 'yanchor': 'top'},
                  geo_scope='usa')
fig.show()
Group.columns = [x.replace('_',' ').title().replace('Addr', 'Address') for x in Group.columns]
Group['Percentage'] = np.round(100* Group['Count'] /Group['Count'].sum(), 2)
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

Colors = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.bar(Group, x= 'Address State', y= 'Percentage', orientation='v',
             color = 'Loan Status', text = 'Percentage', hover_data= Group.columns, color_discrete_sequence= Colors,
             height= 500)
fig.update_traces(marker_line_color=LineColor, marker_line_width=.8, opacity=1,
                  texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(plot_bgcolor= 'white', uniformtext_minsize= 8, uniformtext_mode='hide',
                 title={'text': 'Number of Applications from Each State and the Application Approval Status',
                         'x':0.46, 'y':0.92, 'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(range=[0, 16],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()

Number of Trades Opened in Past 24 Months by Borrowers and the Application Approval Status¶

def mytitle(x):
    x = x.replace('_',' ')
    x = x.title()
    Dict ={' A ':' a ', ' An ':' an ', ' The': ' the ', ' Of ': ' of ', ' In ': ' in '}
    for c in Dict.keys():
        x = x.replace(c, Dict[c])
    return x
      
Feat= 'acc_open_past_24mths'
Group = Data.groupby([Feat,'loan_status'])[Feat].agg('count').to_frame('Count').reset_index()
Feat = mytitle(data_dict[Feat])
Temp = [Feat, 'Loan Status', 'Count']  
Group.columns = Temp
del Temp
Group['Percentage'] = np.round(100* Group['Count'] /Group['Count'].sum(), 2)
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

Colors = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.bar(Group, x= Feat, y= 'Percentage', orientation='v',
             color = 'Loan Status', text = 'Percentage', hover_data= Group.columns, color_discrete_sequence= Colors,
             height= 500)
fig.update_traces(marker_line_color=LineColor, marker_line_width=.8, opacity=1,
                  texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(plot_bgcolor= 'white', uniformtext_minsize= 8, uniformtext_mode='hide',
                 title={'text': 'Number of Trades Opened in Past 24 Months by Borrowers and the Application Approval Status',
                         'x':0.46, 'y':0.92, 'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(range=[0, 14],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()

Borrowers Annual Income Distribution and the Application Approval Status¶

Group = Data[['annual_inc', 'loan_status']]
Group.columns = ['Annual Income', 'Loan Status']
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

C = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.histogram(Group, x = 'Annual Income', color='Loan Status',
                   marginal= 'box', color_discrete_sequence= C, hover_data= Group.columns)
fig.update_traces(marker_line_color= LineColor, marker_line_width=0.2, opacity=1)
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(range=[0, .4e6],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig['layout']['yaxis'].update(range=[0, 2.5e3])
fig.update_layout(plot_bgcolor= 'white',
                  title={'text': 'Borrowers Annual Income Distribution and the Application Approval Status',
                             'x':0.46, 'y':0.95, 'xanchor': 'center', 'yanchor': 'top'}, yaxis_title='Frequency')
fig.show()

Borrowers DTI Score and the Application Approval Status¶

Group = Data[['dti', 'loan_status']]
Group.columns = ['DTI', 'Loan Status']
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

C = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.histogram(Group, x = 'DTI', color='Loan Status',
                   marginal= 'box', color_discrete_sequence= C, hover_data= Group.columns)
fig.update_traces(marker_line_color= LineColor, marker_line_width=0.2, opacity=1)
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(range=[0, 1.5e2],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig['layout']['yaxis'].update(range=[0, 1.4e3])
fig.update_layout(plot_bgcolor= 'white',
                  title={'text': 'Borrowers DTI Score and the Application Approval Status',
                             'x':0.46, 'y':0.95, 'xanchor': 'center', 'yanchor': 'top'}, yaxis_title='Frequency')
fig.show()

Borrowers Employment Length and the Application Approval Status¶

Feat= 'emp_length'
Group = Data.groupby([Feat,'loan_status'])[Feat].agg('count').to_frame('Count').reset_index()
Feat = 'Employment Length'
Temp = [Feat, 'Loan Status', 'Count']  
Group.columns = Temp
del Temp
Group['Percentage'] = np.round(100* Group['Count'] /Group['Count'].sum(), 2)
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

Colors = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.bar(Group, x= Feat, y= 'Percentage', orientation='v',
             color = 'Loan Status', text = 'Percentage', hover_data= Group.columns, color_discrete_sequence= Colors,
             height= 500, width = 750)
fig.update_traces(marker_line_color=LineColor, marker_line_width=.8, opacity=1,
                  texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(plot_bgcolor= 'white', uniformtext_minsize= 8, uniformtext_mode='hide',
                 title={'text': 'Borrowers Employment Length and the Application Approval Status',
                         'x':0.46, 'y':0.94, 'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(range=[0, 40],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()

Borrowers Grade and the Application Approval Status¶

Feat= 'grade'
Group = Data.groupby([Feat,'loan_status'])[Feat].agg('count').to_frame('Count').reset_index()
Feat = Feat.title()
Temp = [Feat, 'Loan Status', 'Count']  
Group.columns = Temp
del Temp
Group['Percentage'] = np.round(100* Group['Count'] /Group['Count'].sum(), 2)
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

Colors = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.bar(Group, x= Feat, y= 'Percentage', orientation='v',
             color = 'Loan Status', text = 'Percentage', hover_data= Group.columns, color_discrete_sequence= Colors,
             height= 500, width = 600)
fig.update_traces(marker_line_color=LineColor, marker_line_width=.8, opacity=1,
                  texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(plot_bgcolor= 'white', uniformtext_minsize= 8, uniformtext_mode='hide',
                 title={'text': 'Borrowers Grade and the Application Approval Status',
                         'x':0.46, 'y':0.94, 'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(range=[0, 30],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()

Purpose of the Loan and the Application Approval Status¶

Feat= 'purpose'
Group = Data.groupby([Feat,'loan_status'])[Feat].agg('count').to_frame('Count').reset_index()
Feat = Feat.title()
Temp = [Feat, 'Loan Status', 'Count']  
Group.columns = Temp
Group[Feat] = Group[Feat].map(lambda x: mytitle(x))

del Temp
Group['Percentage'] = np.round(100* Group['Count'] /Group['Count'].sum(), 2)
Group['Loan Status'] = Group['Loan Status'].map(lambda x: 'Approved' if x == 0 else 'Not Approved')

Colors = ['LightGreen','LightSalmon']
LineColor = 'Black'
fig = px.bar(Group, x= Feat, y= 'Percentage', orientation='v',
             color = 'Loan Status', text = 'Percentage', hover_data= Group.columns, color_discrete_sequence= Colors,
             height= 500)
fig.update_traces(marker_line_color=LineColor, marker_line_width=.8, opacity=1,
                  texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(plot_bgcolor= 'white', uniformtext_minsize= 8, uniformtext_mode='hide',
                 title={'text': 'Purpose of the Loan and the Application Approval Status',
                         'x':0.46, 'y':0.92, 'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(range=[0, 60],
                showgrid=True, gridwidth=1, gridcolor='Lightgray',
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()

	id	issue_d	loan_status	grade	sub_grade	acc_open_past_24mths	addr_state	all_util	annual_inc	avg_cur_bal	...	term	tot_cur_bal	tot_hi_cred_lim	total_acc	total_bal_ex_mort	total_bc_limit	total_cu_tl	total_il_high_credit_limit	verification_status_joint	zip_code
1	153006	2018/5/1	0	B	B2	12	CO	30.0	137000.0	21685.0	...	60 months	346960	407702	30	25092	34000	0	10102	NaN	801xx
2	372899	2018/12/1	0	A	A3	5	NE	50.0	115000.0	4017.0	...	36 months	60248	120390	22	60248	37500	1	79390	NaN	686xx
3	18086	2018/3/1	0	D	D2	21	CA	32.0	88000.0	26711.0	...	36 months	454094	421636	27	149094	24500	4	78336	Source Verified	921xx
4	234509	2018/4/1	0	D	D5	4	MN	53.0	78000.0	2441.0	...	60 months	26850	50648	22	26850	12900	0	28548	NaN	550xx
5	89694	2018/1/1	0	D	D2	6	SC	90.0	80000.0	5920.0	...	36 months	29598	33083	7	29598	3400	0	29683	NaN	297xx
6	175696	2018/5/1	0	B	B4	10	FL	41.0	70000.0	1992.0	...	36 months	23906	58800	14	23906	37200	2	15000	NaN	322xx
7	225284	2018/4/1	1	D	D2	5	MO	50.0	175000.0	10422.0	...	36 months	125061	190200	27	125061	35500	0	95448	NaN	654xx
8	161861	2018/5/1	1	D	D1	7	OH	51.0	65000.0	18684.0	...	36 months	130787	149729	21	44131	14800	0	45207	NaN	442xx
9	55293	2018/2/1	0	C	C1	4	AZ	27.0	61000.0	25910.0	...	36 months	285006	328782	16	14618	9600	1	12353	NaN	852xx
10	348422	2018/7/1	0	A	A5	5	FL	62.0	89000.0	22472.0	...	36 months	224719	252389	22	35543	30800	0	20325	NaN	327xx

Data Type	Features	Count
int64	['id', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'num_il_tl', 'num_rev_accts', 'num_tl_90g_dpd_24m', 'max_bal_bc', 'revol_bal', 'tot_hi_cred_lim', 'total_acc', 'total_bal_ex_mort', 'total_bc_limit', 'total_cu_tl', 'total_il_high_credit_limit', 'tot_cur_bal', 'inq_last_12m', 'mo_sin_old_rev_tl_op', 'loan_status', 'acc_open_past_24mths']	20
float64	['funded_amnt_inv', 'sec_app_open_act_il', 'sec_app_collections_12_mths_ex_med', 'annual_inc', 'percent_bc_gt_75', 'pct_tl_nvr_dlq', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'dti', 'mo_sin_old_il_acct', 'installment', 'all_util']	13
object	['sub_grade', 'grade', 'issue_d', 'addr_state', 'purpose', 'grade.1', 'dti_joint', 'emp_length', 'verification_status_joint', 'emp_title', 'term', 'zip_code']	12